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Google Professional-Cloud-Database-Engineer Exam Syllabus Topics:

SectionObjectives
Ensure reliability, performance, and optimization- Security and compliance
  • 1. Encryption at rest and in transit
    • 2. IAM roles and access control
      - Performance optimization
      • 1. Indexing, query optimization, and caching strategies
        • 2. Workload tuning across database services
          Manage a database solution spanning multiple systems- Operational database management
          • 1. Capacity planning and scaling
            • 2. Monitoring and performance tuning
              • 3. Backup and restore strategies
                Migrate data solutions to Google Cloud- Database migration strategies
                • 1. Online vs offline migration tools (e.g., Database Migration Service)
                  • 2. Lift-and-shift vs re-platforming
                    Design scalable and highly available database solutions- Design for high availability and disaster recovery
                    • 1. Regional vs multi-regional architectures
                      • 2. Replication strategies and failover design
                        - Select appropriate database services (relational, NoSQL, analytical)
                        • 1. Bigtable use cases and schema design
                          • 2. Firestore and document database patterns
                            • 3. Cloud SQL vs AlloyDB vs Spanner selection criteria

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                              Google Cloud Certified - Professional Cloud Database Engineer Sample Questions (Q162-Q167):

                              NEW QUESTION # 162
                              You are configuring a new application that has access to an existing Cloud Spanner database. The new application reads from this database to gather statistics for a dashboard. You want to follow Google-recommended practices when granting Identity and Access Management (IAM) permissions. What should you do?

                              Answer: D

                              Explanation:
                              https://cloud.google.com/iam/docs/overview


                              NEW QUESTION # 163
                              You are the DBA working at a large bank in Europe. Your business-critical banking application runs on Oracle 12.2 in your on-premises data center. You decided to modernize your on- premises Oracle database by migrating to AlloyDB. You must meet the following requirements:
                              - Remain on-premises and maintain open-source portability.
                              - Meet regulatory requirements for data residency.
                              - Support both OLTP and OLAP workloads.
                              - Maintain high availability (HA) mode and automatic failover.
                              What should you do-

                              Answer: C

                              Explanation:
                              On-premises: AlloyDB Omni is designed for on-prem deployments and meets your data residency requirements.
                              HA and Failover: Only the Kubernetes-based AlloyDB Omni deployment with the AlloyDB Omni Kubernetes Operator supports high availability with automatic failover.
                              Open-source portability: AlloyDB Omni is PostgreSQL-compatible, ensuring future flexibility.
                              OLTP + OLAP: AlloyDB supports both transactional and analytical workloads using its vectorized execution and columnar engine features.
                              Regulatory requirements: Staying on-premises aligns with strict data residency and compliance needs common in EU banking environments.


                              NEW QUESTION # 164
                              Your customer has a global chat application that uses a multi-regional Cloud Spanner instance. The application has recently experienced degraded performance after a new version of the application was launched. Your customer asked you for assistance. During initial troubleshooting, you observed high read latency. What should you do?

                              Answer: C

                              Explanation:
                              To troubleshoot high read latency, you can use SQL statements to analyze the SPANNER_SYS.READ_STATS* tables. These tables contain statistics about read operations in Cloud Spanner, including the number of reads, read latency, and the number of read errors. By analyzing these tables, you can identify the cause of the high read latency and take appropriate action to resolve the issue. Other options, such as using query parameters to speed up frequently executed queries or changing the Cloud Spanner configuration from multi-region to single region, may not be directly related to the issue of high read latency. Similarly, analyzing the SPANNER_SYS.QUERY_STATS* tables, which contain statistics about query operations, may not be relevant to the issue of high read latency.


                              NEW QUESTION # 165
                              You are designing a database strategy for a new web application. You plan to start with a small pilot in one country and eventually expand to millions of users in a global audience. You need to ensure that the application can run 24/7 with minimal downtime for maintenance. What should you do?

                              Answer: A

                              Explanation:
                              https://docs.google.com/forms/d/e/1FAIpQLSfZ77ZnuUL0NpU-
                              bOtO5QUkC0cnRCe5YKMiubLXwfV3abBqkg/viewform


                              NEW QUESTION # 166
                              You want to migrate an existing on-premises application to Google Cloud. Your application supports semi- structured data ingested from 100,000 sensors, and each sensor sends 10 readings per second from manufacturing plants. You need to make this data available for real-time monitoring and analysis. What should you do?

                              Answer: A

                              Explanation:
                              Bigtable is a scalable, fully managed, and high-performance NoSQL database service that can handle semi- structured data and support real-time monitoring and analysis. Cloud SQL is a relational database service that does not support semi-structured data. BigQuery is a data warehouse service that is optimized for batch processing and analytics, not real-time monitoring. Cloud Spanner is a relational database service that supports semi-structured data with JSON data type, but it is more expensive and complex than Bigtable for this use case.


                              NEW QUESTION # 167
                              ......

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